Instructions to use cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("unsloth/Meta-Llama-3.1-8B") model = PeftModel.from_pretrained(base_model, "cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f") - Notebooks
- Google Colab
- Kaggle
Download adapter_model.bin from cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f: direct link, hf CLI and curl.
- Browser
- Download file 671 MB
-
https://huggingface.co/cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f/resolve/main/adapter_model.bin
- Command line
-
hf download hf://cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f/adapter_model.bin
-
curl -L -o adapter_model.bin https://huggingface.co/cimol/9239fed7-15ba-4497-bf0f-d387d14ca95f/resolve/main/adapter_model.bin
671 MB
- Xet hash:
- 92194031945832ba206d6ff5161e5c6b417e692c917f4a41a3264b47affd5e1e
- Size of remote file:
- 671 MB
- SHA256:
- a473a4a1c8c23e3380b33605b2f4f883e8da4c49ea3e747d048402ff4d794ab6
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